Large computer networks, often used in cloud computing or other applications, may contain hundreds or thousands of components of several types, such as switches, routers, and hubs. Due to numerous factors, such as the complexity of the components and their numbers, it is likely that at some point, a network component may encounter a fault or otherwise cause problems that degrade the operation of the network. Management of large computer networks may therefore involve identifying network failures and locating the network component or components that contributed to or are otherwise responsible for the errors. However, detecting and, in particular, locating failed or malfunctioning network components remains a challenging endeavor.
Some approaches to locate the failed network components involve using various statistical techniques which may be used to form estimates of attributes. The attributes may identify components involved in a network fault. The success at discovering the network fault may vary between the various statistical techniques. Some of the causes of this variance may include network topology, network fault characteristics (for example percentage loss), and the raw data input from the network. Due to such variance, a given statistical technique may generate incorrect results regarding failed network components.